Data Mining by Jiawei Han

Data Mining: Concepts and Techniques (4th Edition) from The Morgan Kaufmann Series in Data Management Systems is a definitive guide for students, professionals, and researchers looking to explore the field of data mining and knowledge discovery from data (KDD). This book provides a thorough introduction to data mining concepts, principles, and practical applications, offering insights into methods for pattern recognition, classification, clustering, and outlier detection from massive data sets.

The fourth edition introduces a new comprehensive chapter on deep learning, covering topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs). The book also explores advanced topics including data warehousing, preprocessing, dimensionality reduction, and data mining methodologies, making it an essential resource for understanding how to extract valuable insights from big data.

Key Features:

• Comprehensive Coverage: Covers fundamental concepts such as frequent pattern mining, classification, clustering, and outlier detection.
• New Chapter on Deep Learning: Includes the latest deep learning advancements, such as CNNs, RNNs, and training optimization techniques.
• Advanced Data Mining Applications: Explores applications like sentiment analysis, truth discovery, and information propagation.
• Practical Approach: Emphasizes scalability, effectiveness, and real-world applicability of data mining techniques.
• Illustrative Examples: Step-by-step explanations and practical case studies to enhance understanding.
• Emerging Trends: Covers recent advancements in text mining, spatiotemporal data, and graph/network analysis.
• Exercises & Bibliographic Notes: Each chapter includes exercises for hands-on practice and further reading recommendations.

Topics Covered:

1. Data Mining Basics – Concepts, types, and applications of data mining.
2. Data Preprocessing – Data cleaning, transformation, and dimensionality reduction.
3. Data Warehousing & OLAP – Concepts of data warehouses, schemas, and OLAP operations.
4. Pattern Mining – Techniques for finding frequent itemsets, associations, and correlations.
5. Classification Techniques – Decision trees, Bayesian classifiers, and SVMs.
6. Clustering Methods – Hierarchical, density-based, and high-dimensional clustering techniques.
7. Outlier Detection – Identifying anomalies using statistical and machine learning approaches.
8. Deep Learning Concepts – Training models and working with CNNs, RNNs, and GNNs.
9. Future Trends in Data Mining – Mining social media, spatiotemporal data, and ethical considerations.

Ideal For:

• Students pursuing degrees in computer science, data science, and business analytics.
• Professionals and Researchers in AI, machine learning, and big data analytics.
• Data Analysts seeking to enhance their technical knowledge in pattern recognition and classification.
• Industry Practitioners looking for insights into practical applications of data mining techniques.

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Writer                 ✤    Jiawei Han (Author), Jian Pei (Author),
                                    Hanghang Tong (Author)